In Chapter 5 of Naked Economics(one of my texts for school this year), Economics of Information, Charles Wheelan examines the behaviors of individuals, firms and government without perfect information. In Econ 110, we assume that all parties have perfect information, i.e. everyone knows everything they could want to know. In reality, this is rarely the case and people have to rely on mental models, past experiences and statistics.
This leads to the idea of statistical discrimination, a rational form of discrimination in which an individual makes an inference that is correct based on broad statistical trends but; 1. is likely to be wrong in the specific case at hand and 2.) has a discriminatory effect on a group. Some examples of this in action would be an employer assuming that a female applicant is interested in starting a family and would lose money for the firm when she’d eventually require maternity leave or Police officers being especially cautious or brutal with an African-American male because he might be armed and dangerous or even airport security singling out a middle-eastern male and subjecting him to extra searches.
How should society go about reconciling the fact that in certain cases it is rational to discriminate? Is it fair to think of discrimination based on race/ethnicity and gender as necessary evils in the same vein as crime/pollution in the sense that a certain amount is optimal in order to maximize societal benefit? Leave a comment and let me know!